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Detecting adverse high-order drug combinations from individual case safety reports using computational statistics on disproportionality measures\nJules Bangard, Einar Holsbø, Kristian Svendsen, Vittorio Perduca, Étienne Birmelé\n2025-09-29\n\n*Adverse drug interaction detection using individual case safety reports*\n\n[![build and publish](https://github.com/JulesBa-Git/202509-bangard-detecting/actions/workflows/build.yml/badge.svg)](https://github.com/computorg/published-202605-bangard-adverse/actions/workflows/build.yml) [![Creative Commons License](https://i.creativecommons.org/l/by/4.0/80x15.png)](http://creativecommons.org/licenses/by/4.0/)\n\n### Authors\n\n- [Jules Bangard](https://bangard.xyz) (Institut de Recherche Mathématique Avancée, UMR 7501 Université de Strasbourg et CNRS 7 rue René-Descartes, 67000 Strasbourg, France)\n- [Einar Holsbø](https://einar.sh/) (Faculty of Science and Technology, UiT-The Arctic University of Norway, PO, Box 6050 Stakkevollan, N-9037 Tromsø, Norway)\n- [Kristian Svendsen](https://en.uit.no/ansatte/kristian.svendsen/) (Faculty of Health Sciences, UiT the Arctic University of Norway, Tromsø, Norway)\n- [Vittorio Perduca](https://helios2.mi.parisdescartes.fr/~vperduca/) (CNRS, MAP5, Université Paris Cité, F-75006 Paris, France)\n- [Étienne Birmelé](https://irma.math.unistra.fr/~birmele/) (Institut de Recherche Mathématique Avancée, UMR 7501 Université de Strasbourg et CNRS 7 rue René-Descartes, 67000 Strasbourg, France)\n\n### Abstract\n\nAdverse drug reactions linked to the intake of drug combinations are a critical concern in pharmacovigilance, particularly as the controlled environment of clinical trials often lacks the scale and diversity to detect rare events involving multiple medications. While spontaneous reporting systems provide the necessary breadth for post-market surveillance, identifying overrepresented drug cocktails within such large-scale data remains a significant computational challenge. This study introduces a computational framework for the detection of drug cocktails associated with adverse events, leveraging disproportionality analysis on individual case safety reports. By integrating the Anatomical Therapeutic Chemical classification, the framework extends beyond individual drugs to capture hierarchical pharmacological relationships, enabling exploration of the space of drug combinations beyond pairwise analysis. To address biases inherent in existing disproportionality measures, we employ a hypergeometric risk metric, while a Markov Chain Monte Carlo algorithm provides robust empirical p-value estimation for the risk associated with cocktails. A genetic algorithm further facilitates efficient identification of high-risk drug cocktails. A post-treatment step based on penalized logistic regression allows distinguishing true pharmacological interactions from combined individual effects for cocktails of any size. Validation on synthetic and FDA Adverse Event Reporting System data demonstrates the method’s efficacy in detecting established drugs and drug combinations associated with myopathy-related adverse events. Implemented as an R package, this framework offers a reproducible, scalable tool for post-market drug safety surveillance.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcomputorg%2Fpublished-202605-bangard-adverse","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcomputorg%2Fpublished-202605-bangard-adverse","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcomputorg%2Fpublished-202605-bangard-adverse/lists"}